Texas A&M; AgriLife Research and Cropping Systems Program Co-Principal Investigator 1 September 2015 – 31 August 2017 Total award $240,000

The overall goal of this project is to generate preliminary data necessary for effective utilization of UAS-based imaging techniques for crop production and weed management applications. UAS can be equipped to include multi-spectral sensors (3 to 4 bands in the visible and near-infrared/NIR range), hyperspectral sensors (Headwall’s Standard Micro-Hyperspec VNIR 380-1000 nm spectral range), thermal sensors and LIDAR (Light Detection And Ranging), among others. These sensors have a multitude of applications, in areas such as soil, crop, water and weed management in agriculture. However, interpretation of data collected using UAS-based remotely sensed images requires careful consideration of several factors. What is not known is the error estimates between UAS and ground-level data. Such knowledge is key to validate the utility of UAS-based data collection for various applications in agriculture. One of the major limitations so far is the labor-intensive ground data collection and biomass sampling, which will be addressed in this research. We believe that layering UAS data with field measurements would provide rigorous validation of UAS data and provide required knowledge base for widespread implementation of UAS-based data collection. The team will use the state-of-the-art manned/ unmanned ground platform (UGP) in collecting required ground-truthing information.

TECHNICAL OBJECTIVES

Quantification of the growth and development of field crops as affected by soil, irrigation and crop management strategies

Identification and differentiation of weed/plant species, patterns of infestation, and herbicide injury on crops

Air Force Research Laboratory, Air Vehicles Directorate Principal Investigator and Technical Lead 1 September 2015 – 31 December 2015 Total award $22,000

Because of the widely varying flight conditions in which hypersonic vehicles operate and certain aspects unique to hypersonic flight, the development of control architectures for these vehicles presents a challenge. One particular safety concern in hypersonic flight is inlet unstarts, which not only produce a significant decrease in the thrust but also can lead to loss of control and possibly the loss of the vehicle. Previous work on control design for hypersonic vehicles often has involved linearized or simplified nonlinear dynamical models of the aircraft, but a better approach is a nonlinear adaptive dynamic inversion control architecture with a control allocation scheme. This approach was shown to handle time delays, perturbations in stability derivatives, and reduced control surface effectiveness while maintaining tracking performance. The technical objective of this effort is to extend the previous work and develop state-constraint enforcement methods for an adaptive nonlinear dynamic inversion architecture. State constraint enforcement methods are necessary for all classes of aircraft, but are important for hypersonic aircraft. When the engine is operating in what is called “dual-mode,” the isolator is susceptible to over-pressure due to combustion, which can result in an inlet unstart. At other points in the flight envelope, an inlet unstart can occur when certain angle-of-attack or sideslip limits are violated. Initial work into enforcement of envelope constraints has successfully been done in the context of an adaptive dynamic inversion control law that assumes full-state feedback.

TECHNICAL OBJECTIVES

Develop the formal theory for enforcement of both state and input constraints (position and rate limits) while maintaining stability.

Address an extension of state-constraint enforcement in an output-feedback adaptive dynamic inversion architecture. In this case, the state that must be constrained may not be directly measurable and therefore have to be estimated.

This project seeks to exploit derived angle-of-attack (AOA, ##\alpha##) and flightpath angles (##\gamma##) from low cost Attitude Heading Reference System (AHRS) COTS systems found in GA aircraft. The feasibility of derived AOA will be evaluated for use cases of displays, envelope protection, and fly-by-wire flight control systems. It is expected that the results of this work will be a) recommended minimum performance standards for the algorithm and AHRS device, and b) the criteria for each use case when using AHRS that can be codified into a standard or a circular. The aircraft considered will be Part 23 aircraft (such as C-172, Cirrus SR-22, Lancair 350 and light jets). In addition, hybrid aircraft (Part 23 and part 27) may be considered. The COTS AHRS to be evaluated are those typically found in aircraft and will include 3 systems, 1 of each from the following categories. 1) Installed AHRS (such as Garmin, Aspen, Avidyne) 2) Portable AHRS (such as iLevil, Stratus, Sagetech) 3)Low Cost AHRS typically found on UAV’s (such as Pixhawk, Airware). Phase I will consist of an offline simulation study in the context of intended function, and benchmarks of existing sensors. The simulation engine which will generate inputs to the algorithms is XPlane 10, used in the VSCL Engineering Flight Simulator. The initial use will be an AHRS modeled as a fully integrated input-output sensor system.

TECHNICAL OBJECTIVES

The proposed work seeks to understand how the various COTS AHRS component characteristics affect the usability of derived AOA solutions by conducting a simulation study which will investigate:

It is imperative that infrastructure is properly maintained in order to accommodate the demand of today’s modern society. Information corridors, transportation corridors and pipelines are intrinsically a part of the modern society as many daily functions depend wholly and completely on their use. The need is to conduct assessments of physical infrastructure such as railroads, bridges, roads, pipelines, refineries, from a specified height above the ground. The assessments consist of structural integrity, wear, and safety inspections. The assessments are traditionally conducted by an on the ground mobile team with all equipment being field portable unit or units. However, many of the assessment items are sited in remote areas, areas difficult to access from the ground, or possibly located in hazardous areas. Therefore companies are deploying specialized teams and helicopters or short distance UAS’s to capture images of the infrastructure elements. Sensors that operate in both the infrared (IR) and visible spectrums are typically used. Additional sensors such as Laser Interferometry Detection and Ranging (LIDAR) offer an attractive capability to image an assessment item in three dimensions with defects or damage precisely located on the item, and should be considered. The Center for Autonomous Vehicles and Sensor Systems (CANVASS) is conducting an effort that will culminate in a technology demonstration of a UAS system for infrastructure assessment. All applicable Federal Aviation Regulations (FAR) will be adhered to so that the system can be operated within domestic airspace. The demonstration will be conducted in realistic outdoor laboratory environments and controlled condition field environments. The effort will focus on integrated multi-spectral sensor (optical, infrared) and video analytics, for the purpose of demonstrating the technology and its benefits. Two preferred systems are being used for all sensor integration and infrastructure elements testing. One is a fixed-wing UAS with a multi-spectral sensor for larger area coverage. This is the CANVASS owned Anaconda UAS, a proven UAS that possess the payload capacity, flight performance (45 minute endurance), and rough field takeoff and landing capability for the proposed work. The second UAS is an octocopter that will also carry the multi-spectral sensor for close-in imaging. This vehicle is the Spread Wings S1000+ professional quality octocopter. The S1000+ weighs just 4.4kg and has a maximum takeoff weight of 11kg. It can easily carry the Multispectral camera plus future cameras and payloads. Used with a 6S 15000mAh battery, it can fly for up to 15 minutes. The UAS systems will be validated, verified, and tested in a series of five flights that will be conducted at the Riverside Range, TAMU Riverside Campus. The Riverside Range is part of the FAA Lone Star UAS Center. The purpose is to verify basic system operation, and collect data on test items of interest at known locations on the test range.

The Universal Access Transceiver (UAT) ADS-B is a cooperative surveillance technology in which an aircraft determines its position via satellite navigation and periodically broadcasts it, enabling it to be tracked. The information can be received by air traffic control ground stations as a replacement for secondary radar. It can also be received by other aircraft to provide situational awareness and allow self separation. ADS-B is the first core technology within NextGen, the ongoing program to increase the efficiency, capacity and safety of the world’s airspace systems. ADS-B is an Air Traffic Management (ATM) Surveillance system that will replace traditional radar-based systems. It provides greater accuracy and wider coverage to safely allow reduced separation, more efficient routing and other benefits. The Vehicle Systems and Control Laboratory (VSCL) will conduct test and evaluation of the RANGR ADS-B System, to consist of flight tests to collect data and assess suitability of the UAT for use in NextGen airspace and operations.

As part of its development of integrated sensor systems for UAS platforms, Intuitive Machines requires flight testing of its sensor system for a specific application, such as agriculture, and an independent engineering assessment of system’s capability to provide the desired results. This project will conduct flight testing of an integrated sensor system for an agriculture application followed by an engineering assessment of the system’s capabilities. Intuitive Machines is providing the sensor system, and agricultural and aerospace researchers from Texas A&M University are teaming to conduct the flight testing and engineering assessment. Texas A&M University researchers will define the requirements of the sensor system for an agriculture application, integrate the sensor system into the Pegasus II UAS platform, conduct flight tests over a suitable agriculture field, and then, assess the system’s capabilities to meet the specified requirements.